Matcha通过多阶段流匹配实现快速精准的分子对接，兼顾物理合理性。
Matcha: Multi-Stage Riemannian Flow Matching for Accurate and Physically Valid Molecular Docking
- 分三阶段在不同几何空间进行流匹配，逐步优化对接构象。
- 相比主流方法，物理合理性显著提升，推理速度比大模型快31倍。
- 适合需要高精度且快速生成合理对接构象的药物设计场景。
精确预测蛋白质-配体结合构象对基于结构的药物设计至关重要，但现有方法难以兼顾速度、准确性和物理合理性。我们提出Matcha，一种新型分子对接流程，结合多阶段流匹配与物理感知后处理。该方法包含三个连续阶段，分别在ℝ³、SO(3)和SO(2)几何空间中实施流匹配模型，逐步优化对接预测。通过GNINA能量最小化提升预测质量，并应用无监督物理有效性过滤器剔除不合理的构象。在所有基准测试中，Matcha均表现出更优的物理合理性。此外，本方法推理速度约为现代大规模共折叠模型的31倍。模型权重与推理代码已公开于https://github.com/LigandPro/Matcha。
原文摘要 · Abstract (English)
Accurate prediction of protein-ligand binding poses is crucial for structure-based drug design, yet existing methods struggle to balance speed, accuracy, and physical plausibility. We introduce Matcha, a novel molecular docking pipeline that combines multi-stage flow matching with physically-aware post-processing. Our approach consists of three sequential stages applied consecutively to progressively refine docking predictions, each implemented as a flow matching model operating on appropriate geometric spaces ($\mathbb{R}^3$, $\mathrm{SO}(3)$, and $\mathrm{SO}(2)$). We enhance the prediction quality through GNINA energy minimization and apply unsupervised physical validity filters to eliminate unrealistic poses. Compared to various approaches, Matcha demonstrates superior physical plausibility across all considered benchmarks. Moreover, our method works approximately 31 times faster than modern large-scale co-folding models. The model weights and inference code to reproduce our results are available at https://github.com/LigandPro/Matcha.
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